Statistical of problem solving tools useful in achieving

Statistical Process Control (SPC) is a  scientific, data-driven methodology
methodology for measuring and controlling quality during the manufacturing
process. Quality data in the form of Product or Process measurements are
obtained in real-time during manufacturing. This data is then plotted on a
graph with pre-determined control limits. Control limits are determined by the
capability of the process, whereas specification limits are determined by the
client’s needs. Statistical Process Control (SPC) is also said to be a powerful
collection of problem solving tools useful in achieving process stability and
improving capability through the reduction of variability, Montgomery and
Runger (2007) . It is a technique used to determine whether a process is in statistical
control.In most manufacturing and non –manufacturing systems one of the tools
that has been widely used  for  Quality Control is the Shewhart’s Statistical
Process Control Charts. Shewhart charts are typically used to distinguish
between variations due to special causes from variations due to

common causes. Special causes are referred to as assignable
causes like sporadic problems such

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as the failure of a particular machine or a mistakenly
recorded measurement and these are identifiable and correctable .

Common causes are problem inherent in every process and
somehow cannot be avoided. The process is

said to be in statistical control when the special causes
have been identified and eliminated, and

once statistical control has been established, Shewhart
charts can be used to monitor the process

for the occurrence of future special causes and to measure
and reduce the effects of common

causes, Montgomery (2005). These charts have been used and
are still being used to monitor process performance ,Process monitoring also
plays a key role in ensuring that the plant performance satisfies the operating
objectives.The general objectives of process monitoring are: Routine Monitoring
-Ensure that process variables are within specified limits. Detection and
Diagnosis. -Detect abnormal process operation and diagnose the root cause.
Preventive Monitoring.-Detect abnormal situations early enough so that corrective
action can be taken before the process is seriously upset.Shewhart Control
charts are referred to as Univariate Control charts because they observe a
single variable at a given time,Univariate charts consider individual quality
measurement sources and as a result they have several limitations.

 

Applying univariate SPC results in the majority of the
variables collected on a process not being monitored. Furthermore, the
monitored variables are not necessarily independent hence examining a limited group
of variables, one at a time, makes the identification and interpretation of
process malfunctions extremely difficult, and consequently the results of the
analysis may provide misleading information.

 

Multivariate statistical process control methods (MSPC)
address some of the limitations of univariate monitoring techniques by
considering all the data simultaneously and extracting information on the
‘directionality’ of the process variations.For most industrial processes two or
more quality variables are important, and they can be highly correlated. For
these situations, multivariable SPC techniques can offer significant advantages
over the single-variable methods.

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